@incollection{SchenkerVierhausBorndoerferetal., author = {Schenker, Sebastian and Vierhaus, Ingmar and Bornd{\"o}rfer, Ralf and F{\"u}genschuh, Armin and Skutella, Martin}, title = {Optimisation Methods in Sustainable Manufacturing}, series = {Sustainable Manufacturing - Challenges, Solutions and Implementation Perspectives}, booktitle = {Sustainable Manufacturing - Challenges, Solutions and Implementation Perspectives}, editor = {Stark, Rainer and Seliger, G{\"u}nther and Bonvoisin, J{\´e}r{\´e}my}, publisher = {Springer Verlag}, address = {Cham}, isbn = {978-3-319-48513-3}, issn = {2194-0541}, doi = {10.1007/978-3-319-48514-0_15}, pages = {239 -- 253}, language = {en} } @incollection{FuegenschuhHomfeldJohannetal., author = {F{\"u}genschuh, Armin and Homfeld, Henning and Johann, Marc and Sch{\"u}lldorf, Hanno and Stieber, Anke}, title = {Use of Optimization Tools for Routing in Rail Freight Transport}, series = {Handbook of Optimization in the Railway Industry}, booktitle = {Handbook of Optimization in the Railway Industry}, editor = {Bornd{\"o}rfer, Ralf and Klug, Torsten and Lamorgese, Leonardo and Mannino, Carlo and Reuther, Markus and Schlechte, Thomas}, publisher = {Springer International Publishing}, address = {Berlin}, isbn = {978-3-319-72152-1}, issn = {0884-8289}, doi = {10.1007/978-3-319-72153-8_8}, pages = {161 -- 179}, language = {en} } @incollection{FuegenschuhMarahrensJohannsmannetal., author = {F{\"u}genschuh, Armin and Marahrens, S{\"o}nke and Johannsmann, Leonie M. and Matuszewski, Sandra and M{\"u}llenstedt, Daniel and Schmidt, Johannes}, title = {Using Computer-Generated Virtual Realities, Operations Research, and Board Games for Conflict Simulations}, series = {Simulation and Wargaming}, booktitle = {Simulation and Wargaming}, editor = {Turnitsa, Charles and Blais, Curtis Blais and Tolk, Andreas}, publisher = {John Wiley \& Sons, Inc.}, address = {Hoboken, NJ}, isbn = {978-1-119-60478-5}, doi = {10.1002/9781119604815}, pages = {273 -- 287}, language = {en} } @incollection{BeisegelBuhlIsraretal., author = {Beisegel, Jesse and Buhl, Johannes and Israr, Rameez and Schmidt, Johannes and Bambach, Martin and F{\"u}genschuh, Armin}, title = {Mixed-Integer Programming Models for Two Metal Additive Manufacturing Methods}, series = {Mathematical Methods for Objects Reconstruction}, booktitle = {Mathematical Methods for Objects Reconstruction}, editor = {Cristiani, Emiliano and Falcone, Maurizio and Tozza, Silvia}, publisher = {Springer}, address = {Heidelberg}, isbn = {978-981-9907-75-5}, pages = {121 -- 155}, language = {en} } @incollection{SchmidtFuegenschuh, author = {Schmidt, Johannes and F{\"u}genschuh, Armin}, title = {A column generation approach for weighted set packing problems with forbidden pairs}, series = {Operations Research Proceedings 2023 : selected papers of the Annual International Conference of the German Operations Research Society (GOR), Germany, August 29-September 1, 2023}, booktitle = {Operations Research Proceedings 2023 : selected papers of the Annual International Conference of the German Operations Research Society (GOR), Germany, August 29-September 1, 2023}, publisher = {Springer Nature Switzerland}, address = {Cham}, isbn = {978-3-031-58405-3}, issn = {2731-040X}, doi = {10.1007/978-3-031-58405-3_14}, pages = {107 -- 113}, abstract = {The weighted set packing problem (WSPP) is a variant of the well-known set packing problem, assigning a weight to every considered subset of a given finite base set. Its solution is a maximum sum-weight, element-disjoint covering of the base set. Using its formulation as a binary linear program (BLP), every column of the constraint matrix describes one of the given subsets. Thus, column generation (CG) provides a good solution method, generating iteratively only promising subsets as candidates for the covering. We consider an extension of the WSPP, where forbidden pairs are taken into consideration, i.e., there can be pairs of subsets that cannot be chosen at the same time, and adapt CG to derive a new solution algorithm to solve the resulting BLP. At each iteration, the optimal solution obtained with CG is checked for potential conflicts and either they are resolved locally without changing the objective function value or a new linear constraint forbidding the conflicting pair of columns is added. Then the CG procedure starts again. The applicability of this approach is shown for a team orienteering problem involving two unmanned aerial vehicles that must maintain safety distances between them.}, language = {en} }